_base_ = "../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py"
model = dict(
    rpn_head=dict(
        _delete_=True,
        type="GARPNHead",
        in_channels=256,
        feat_channels=256,
        approx_anchor_generator=dict(
            type="AnchorGenerator",
            octave_base_scale=8,
            scales_per_octave=3,
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64],
        ),
        square_anchor_generator=dict(
            type="AnchorGenerator", ratios=[1.0], scales=[8], strides=[4, 8, 16, 32, 64]
        ),
        anchor_coder=dict(
            type="DeltaXYWHBBoxCoder",
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[0.07, 0.07, 0.14, 0.14],
        ),
        bbox_coder=dict(
            type="DeltaXYWHBBoxCoder",
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[0.07, 0.07, 0.11, 0.11],
        ),
        loc_filter_thr=0.01,
        loss_loc=dict(
            type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0
        ),
        loss_shape=dict(type="BoundedIoULoss", beta=0.2, loss_weight=1.0),
        loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type="SmoothL1Loss", beta=1.0, loss_weight=1.0),
    ),
    roi_head=dict(bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))),
    # model training and testing settings
    train_cfg=dict(
        rpn=dict(
            ga_assigner=dict(
                type="ApproxMaxIoUAssigner",
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                ignore_iof_thr=-1,
            ),
            ga_sampler=dict(
                type="RandomSampler",
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False,
            ),
            allowed_border=-1,
            center_ratio=0.2,
            ignore_ratio=0.5,
        ),
        rpn_proposal=dict(nms_post=1000, max_per_img=300),
        rcnn=dict(
            assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
            sampler=dict(type="RandomSampler", num=256),
        ),
    ),
    test_cfg=dict(rpn=dict(nms_post=1000, max_per_img=300), rcnn=dict(score_thr=1e-3)),
)
optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
